Scrutiny on the data supply chain

In the data supply chain, businesses within the financial services space need to understand and to audit what happens to the data across the process.

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Although the term ‘supply chain’ is most commonly associated with manufacturing, it is now frequently being applied to the management of data within financial services firms. While these firms deal with growing volumes of raw data as opposed to raw materials, the principles of the supply chain remain the same.

As with any supply chain, being able to trace materials or data across the whole process is very important. In the data supply chain, businesses within the financial services space need to understand and to audit what happens to the data across the process, who has looked at it, how it has been verified and must keep a full record of any decisions that are made. Ultimately, they need to ensure traceability, that they can track the journey of any piece of data across the supply chain and see both where it has been and where it ends up.

The advantage for financial services firms who reach the end of this data supply chain is that the result of this process supports informed opinion that in turn drives risk, trading and business decisions. Bringing the data together in this way is important for many financial services firms. After all, the reality is that these businesses, today even more than pre-crisis, typically have many functional silos of data in place, a problem made still worse by the preponderance of mergers and acquisitions taking place across the sector in recent times. Today, it is commonplace that market risk may have its own database, so too credit risk, finance stress testing and product control. In fact, every business line may have its own data set. Moreover, all these different groups will all also have their own take on data quality.

Many financial services firms increasingly appreciate that this situation is no longer sustainable. The end to end process outlined above should help to counteract this but why is it happening right now?

There’s no doubt that regulation is a key driver. In recent years, we have seen the advent of the Targeted Review of Internal Models (TRIM) and the Fundamental Review of the Trading Book (FRTB) both of which demand that a consistent data set is in place. It seems likely that the costs and the regulatory repercussions of failing to comply will go up over time.

Additionally, it is becoming costly to keep all these different silos alive to support it. The staff who originally developed them are often no longer with the business or have a different set of priorities, making for a very costly infrastructure. Lastly, there is a growing consensus that if a standard data dictionary and vocabulary of terms and conditions are used within the business, and there is common access to the same data set, this will inevitably help to drive a better and more informed decision-making process across the business.

Developing a solution

In order to address these issues and overcome the data challenges outlined above, businesses should begin by ensuring that they have a 360˚ view of all the data that is coming into the organisation. They need to make sure they know exactly what data assets there are in the firm – what they already have on the shelf, what they are buying and what they are collecting or creating internally. In other words, they need to have a comprehensive view of exactly what data enters the organisation, how and when it does and in what shape and form.

This is far less trivial than it might sound because in large firms in particular, often due to organisational or budgetary fault lines, organisations may often have sourced the same data feed multiple times, or they might find that the same data product or slight variations of it may be brought into a business on multiple occasions or via different channels.

Therefore, firms need to be clearer not only about what data they are collecting internally but also what they are buying. If they have a better understanding of this, they can make more conscious decisions about what they need and what is redundant and prevent a lot of ‘unnecessary noise’ when it comes to improving their data supply chain.

They also need to be able to verify the quality of the data which effectively means putting in place a data quality framework that encompasses a range of dimensions from completeness to timeliness, accuracy, consistency and traceability.

Of course, to deal with all these data supply chain issues businesses need to have the right governance structure and organisational model in place. Consultants can help here in advising on processes and procedures and ensure for example that the number of individual departments independently sourcing data is reduced and there is a clear view in place of what is fit for purpose data.

If the right processes and procedures are in place, however, alongside a good governance structure, the organisation can start to think about a technological solution.

The use of technology

Technology can play a key role in helping organisations to get a better handle on their data supply chains. For most businesses, a primary requirement is to have good data sourcing and integration capability in place. This means systems that understand financial data products but also the different data models and schemes that are in place to identify instruments, issuers, taxonomies and financial product categorisations.

Organisations also need the ability to support the workflow process and workflow, as well as data reporting capabilities. Technology chosen to fulfil these roles must be capable of providing metrics on the impact of all the different data sources the organisation has bought, what benefits it has achieved from those sources; what kind of quality are they and what gaps are there in the data, and where is the organisation in providing this data to business users for ad-hoc usage.

In addition to understanding and monitoring their supply chains and ensuring that an auditing and traceability element is in place, financial services firms must also guarantee that data governance and data quality checking is fully implemented. After all, to get the most from their data supply chains they must make the data itself readily available to users to browse, analyse and support decision-making processes that ultimately contribute to driving business advantage and competitive edge.